Skip to main content

Python bindings for the MMG software

Project description

mmgpy

PyPI conda-forge Python License Docs codecov

mmgpy brings the power of MMG mesh adaptation to Python. Generate, optimize, and refine 2D, 3D, and surface meshes with a clean API.

import mmgpy

mesh = mmgpy.read("input.vtk")
mesh.remesh(hmax=0.1)
mesh.save("output.vtk")

Mechanical piece remeshing

Try It

No installation needed — run directly with uvx:

# Remesh a mesh file
uvx mmgpy input.stl -o output.mesh -hmax 0.1

# Launch the interactive UI
uvx --from "mmgpy[ui]" mmgpy-ui

Installation

The recommended way to install mmgpy:

uv pip install mmgpy

This uses pre-built wheels from PyPI that bundle all native libraries (MMG, VTK) — no compiler needed.

Other install methods

# pip
pip install mmgpy

# conda-forge
conda install -c conda-forge mmgpy

# With UI support
uv pip install "mmgpy[ui]"

Using uv for project management

uv add mmgpy                 # add to project dependencies
uv tool install mmgpy        # install CLI tools globally
uv tool install "mmgpy[ui]"  # install CLI tools + UI globally

PyPI vs conda-forge

PyPI (pip/uv) conda-forge (Linux/macOS)
Install speed Fast (pre-built wheels) Slower (solver + download)
Dependencies Bundled (self-contained) Shared across packages
Disk usage Larger (duplicate VTK/libs) Smaller in conda environments
Best for Quick setup, isolated use, CI Scientific stacks sharing VTK/NumPy

Use PyPI (uv pip install) for the fastest setup. Use conda-forge when you already have a conda environment with VTK, PyVista, or other scientific packages.

Lagrangian motion (boundary-driven mesh displacement) is available on every channel via mmgpy.move_mesh, with a built-in Laplacian propagator and an optional elasticity propagator backed by fedoo (uv pip install "mmgpy[fem]").

Features

  • Multi-dimensional — 2D triangular, 3D tetrahedral, and surface meshes
  • Local refinement — Control mesh density with spheres, boxes, cylinders
  • Anisotropic adaptation — Metric tensors for directional refinement, including least-squares Hessian recovery from a scalar field
  • Level-set discretization — Extract isosurfaces from implicit functions
  • Lagrangian motion — Move boundaries and remesh, with Laplacian or (optional) elasticity-based propagation
  • PyVista integration — Visualize and convert meshes seamlessly
  • 40+ file formats — VTK, STL, OBJ, GMSH, and more

Usage

Basic Remeshing

import mmgpy

mesh = mmgpy.read("input.mesh")
result = mesh.remesh(hmax=0.1)

print(f"Quality: {result.quality_mean_before:.2f}{result.quality_mean_after:.2f}")
mesh.save("output.vtk")

Local Sizing

mesh = mmgpy.read("input.mesh")

# Fine mesh near a point
mesh.set_size_sphere(center=[0.5, 0.5, 0.5], radius=0.2, size=0.01)

# Fine mesh in a region
mesh.set_size_box(bounds=[[0, 0, 0], [0.3, 0.3, 0.3]], size=0.02)

mesh.remesh(hmax=0.1)

Typed Options

from mmgpy import Mmg3DOptions

opts = Mmg3DOptions(hmin=0.01, hmax=0.1, hausd=0.001)
mesh.remesh(opts)

# Or use presets
mesh.remesh(Mmg3DOptions.fine())

Visualization

mesh.plot()  # Quick plot with edges

# Or for custom plotting:
import pyvista as pv
plotter = pv.Plotter()
plotter.add_mesh(mesh.vtk, show_edges=True, color="lightblue")
plotter.show()

Command Line

MMG executables are included and available after installation:

# Auto-detect mesh type
mmg input.mesh -o output.mesh -hmax 0.1

# Or use specific commands
mmg3d input.mesh -o output.mesh -hmax 0.1
mmgs surface.stl -o refined.mesh -hausd 0.001
mmg2d domain.mesh -o refined.mesh -hmax 0.05

# Check versions
mmg --version

The _O3 suffix variants (mmg3d_O3, etc.) are also available for compatibility.

Gallery

Surface remeshing

Smooth surface optimization

3D quality improvement

Documentation

kmarchais.github.io/mmgpy

Contributing

Contributions are welcome! See CONTRIBUTING.md for development setup, coding standards, and the pull request process.

License

MIT

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mmgpy-0.13.0.tar.gz (18.1 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

mmgpy-0.13.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.14tmanylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp314-cp314-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.14Windows x86-64

mmgpy-0.13.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.14manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp314-cp314-macosx_11_0_arm64.whl (7.5 MB view details)

Uploaded CPython 3.14macOS 11.0+ ARM64

mmgpy-0.13.0-cp313-cp313-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.13Windows x86-64

mmgpy-0.13.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp313-cp313-macosx_11_0_arm64.whl (7.5 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

mmgpy-0.13.0-cp312-cp312-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.12Windows x86-64

mmgpy-0.13.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp312-cp312-macosx_11_0_arm64.whl (7.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

mmgpy-0.13.0-cp311-cp311-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.11Windows x86-64

mmgpy-0.13.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp311-cp311-macosx_11_0_arm64.whl (7.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

mmgpy-0.13.0-cp310-cp310-win_amd64.whl (4.1 MB view details)

Uploaded CPython 3.10Windows x86-64

mmgpy-0.13.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (9.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.27+ x86-64manylinux: glibc 2.28+ x86-64

mmgpy-0.13.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl (9.4 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.26+ ARM64manylinux: glibc 2.28+ ARM64

mmgpy-0.13.0-cp310-cp310-macosx_11_0_arm64.whl (7.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

File details

Details for the file mmgpy-0.13.0.tar.gz.

File metadata

  • Download URL: mmgpy-0.13.0.tar.gz
  • Upload date:
  • Size: 18.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0.tar.gz
Algorithm Hash digest
SHA256 de6e7500d8606bea932d40a3f7d5ea0e4bbcb549d55cb5c83d364894423c7b8f
MD5 8430c45f160712bfbd37ddfe25c814d1
BLAKE2b-256 0cd51c467ef57423d7236d3cafd6658130f746a7023e41b7b246e0adcb2104d5

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ee835e8b9683afcd3f1c2fdae8074e1e4a3ae29dd0a1395c7f21466c6e5fb826
MD5 be2fc0237ae6d6c312cb58a466cc4673
BLAKE2b-256 9ce52b9d1affe8f2d9549cca005bc2f88d280ac1a6ef16f5d25d4f3fdbfd28f5

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314t-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 3463ff9c47ab838f0f351c9f9c06a99512e28e0d65971f169569d836979c8f62
MD5 49db9c10e72474ea9253e28aaaf64d42
BLAKE2b-256 fd4f92442b892056523d219d90fdca269192a4a4564b4b3465993d09161d9440

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.13.0-cp314-cp314-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.14, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314-win_amd64.whl
Algorithm Hash digest
SHA256 6f6183dff93211807fe847fbec10e7621bfd1cebf31ecbcf0c428894766471ad
MD5 8b29dec62ddc74e59b3c914200ff94d8
BLAKE2b-256 9db9c14513aacb76f5e9525bc2d2fe9fbc29f3a069dfaf261a1ebef4506f6d65

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4a121321293518ad218385b9bd572963aa553651e841dbb9c59dc6d7f9cd4cd7
MD5 acab0d3799ff4d4558a0187c1d52e234
BLAKE2b-256 c5c852e8133ba5166e18a4fab2547b3b4e154d7ae284e2cbbb0c543f2f0e0bad

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 8fdd174531164470934d23f823b14523dca95e298b1f09960eac182716e82b22
MD5 e2a5db08499e22561693dbb3875d92fa
BLAKE2b-256 e535c040d3eadbebb0ad7428d60ba6e3ca6a9a6b7e774089a9c3c768c116fbe7

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp314-cp314-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp314-cp314-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a924f148857bd792245b86a523b251f9f67329074606ed2fd9ac60b415229e10
MD5 921c8be8d1e4bb4bd2c893263da77491
BLAKE2b-256 6f313d524b6570772b5a5047ee6d2f822febdc533e93f19fddc37e698e2aea83

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.13.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 5dbf1e7bd03d7f8164ea845d9eba1409b22fa40b14f6a0b745667a938557b834
MD5 dbec249922cd4f81ebd05da894e4c03a
BLAKE2b-256 38919f7d96c9c2c3e7a59f2ee84bdac60bcd0b157d06fa40104bed81ac699a91

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 d01a5ed3f101c3b7324becdf07c1938d5f157e4ed6d87651db8305a13c479550
MD5 a1f825739367110e6a721aaace25f413
BLAKE2b-256 f93ccd17b5e3f836db82b3fd20a3267af983138a2973099bc51f0e4484b57570

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 b27a90990cbd76a59d7091ba1650b7c0c5628380d0c25962b87977cfc060a08b
MD5 036f4bf53187d019ac60ef3615b1bced
BLAKE2b-256 9276fd41b89231fbe417ed93c2ac66b28c295f3ff0fc2b811d9263c23097b2bb

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 dd3cd9de8c5b09ab2a55ba359c2c8b88f27dddcbf56980e903631d3916b81645
MD5 69524cbd685caed1c747144adfd8bc17
BLAKE2b-256 528236d584491d99b968dad72fb096167eb751c2aa69830434cb1113879d77e6

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.13.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 aebadf8428dbca78d7c729538928b6bedf044ea174aab27fabc1826ecfc192f6
MD5 31fa44169fad4566e685fded881954e7
BLAKE2b-256 2adccb9be0dbf58dc6a71dd2c46abfd7fa8b5bcff1f86c57c6b89bf606631e95

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 36d864d88334d71f1b6a6acb1284c5cf6fbd7c5994c70333676740fd957384fb
MD5 0e8d7908088e247d7d47e6a5a2613568
BLAKE2b-256 0dd613b6cbc4b36eca0dfe3d295286de4ed849de5f8d0630e7c153bd45332fbb

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 28eee0fed8e5b0c03829b5bec75f739fa30d3a2d9786aa471f74b837264a2e8b
MD5 5fefb471b3e8986b5053680a986b0885
BLAKE2b-256 84ec9df7b18aeba2797f66d4bff19a496a577f0ecd617c0ce2c3854e3ff5694d

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0a676fef049a551d447c0346699954ae0e66bc7d945be08b5c44ad0cf3885109
MD5 bd12f3aad34fdf4f831278e7436a3a73
BLAKE2b-256 73a0b071d53310081c90ff1c71dc0e3ec41fa8f0c7adbb7f6872182def363704

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.13.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 72c02ff3ecc298eb5c2242feb47e00d65a90135a8ebcb54b322a1081d37786af
MD5 f96071c0a9fb00453f87b4610844dd5e
BLAKE2b-256 a277be7b247b6f75bc197e4b2768c832c963e16a9b35884f6bfbbecd30f950c5

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4ed0aa81079e74663d9c1b6f957fb41eed668b2f88762303d12562a709410086
MD5 7c3f9076baffb5ed51eb94ddac36d4dd
BLAKE2b-256 8a999428c9a2b173051101f9e23d655e813707061ebd19beef8ac56622d6f5bf

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 0477a3d7bb21421bedb38443dbca92bb9385c7b7e561cdb4563f8d261c2cbb22
MD5 43da233db2277594cd6de8414b04ff33
BLAKE2b-256 8f1d022f690ee75e83a97a3708f366c2fef46e315e4533f18cbddcbeb62230e4

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ba418d494f2976989f22944a19a5358e2a0144c033e4c1d75c77fcef6800e3b0
MD5 7190ee160836b9428cfc28a0f917ff8e
BLAKE2b-256 f745e685f03eb279d6478c6dabac888405d6bd471a4c2e61d2d015c48486fee6

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: mmgpy-0.13.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.1 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for mmgpy-0.13.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 6005ab8d27cabcd59306758cd8f06739a37900c1169a47734700ea15e1963125
MD5 42d8d9a183f940a1aad744b3904e1d1b
BLAKE2b-256 ef84d61dcaa81daa4dbdffff39bdec1d4f961978f48c7ac0c710442764310eb7

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 d4b241ecee8fd9fb9e757def4716e3c95c258998c4ea2888800e3cca9fab446d
MD5 ba6f8637b4d66f66da0251b350d17e92
BLAKE2b-256 f717e1f459724857d012b738187b965d32aff12a7e2e3171e9056ae098fcb3cd

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl
Algorithm Hash digest
SHA256 ee256b1ad8900c0d2c578699ca4aab9dc50f4e05abcf3f7f80a041d53bfd70e3
MD5 6378e7f6b4859c2210344f012e54024d
BLAKE2b-256 6d79f5a9371b1e3471d06d01ec77a17944b62032c4c0392b43f2091b6b20dd2a

See more details on using hashes here.

File details

Details for the file mmgpy-0.13.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for mmgpy-0.13.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 71a9c5b14f4752647888f3b75d4cf4868b844299d6b132d65fc0c3ceed2ed748
MD5 20223d1dc75be87f9c2a8f0c66f0aeca
BLAKE2b-256 79c7edacf67ca1404e4a8f0004e93be32bf5b14ab9c63a1e77738837073a3ef1

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page